{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "from qmg.generator import MoleculeGenerator\n",
    "from qmg.utils import ConditionalWeightsGenerator\n",
    "from rdkit import RDLogger\n",
    "import numpy as np\n",
    "RDLogger.DisableLog('rdApp.*')\n",
    "\n",
    "# parameter settings for epoxide derivatives\n",
    "num_heavy_atom = 6\n",
    "random_seed = 3\n",
    "smarts = \"[O:1]1[C:2][C:3]1\"\n",
    "disable_connectivity_position = [1]\n",
    "num_sample = 10000\n",
    "\n",
    "cwg = ConditionalWeightsGenerator(num_heavy_atom, smarts=smarts, disable_connectivity_position=disable_connectivity_position)\n",
    "random_weight_vector = cwg.generate_conditional_random_weights(random_seed)\n",
    "# mg = MoleculeGenerator(num_heavy_atom, all_weight_vector=random_weight_vector)\n",
    "# smiles_dict, validity, diversity = mg.sample_molecule(num_sample)\n",
    "# print(smiles_dict)\n",
    "# print(\"Validity: {:.2f}%\".format(validity*100))\n",
    "# print(\"Diversity: {:.2f}%\".format(diversity*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Descriptors\n",
    "\n",
    "class FitnessCalculator():\n",
    "    def __init__(self, task, distribution_learning=True):\n",
    "        self.task = task\n",
    "        self.distribution_learning = distribution_learning\n",
    "\n",
    "    def calc_property(self, mol):\n",
    "        if self.task == \"qed\":\n",
    "            return Descriptors.qed(mol)\n",
    "\n",
    "    def calc_score(self, smiles_dict: dict):\n",
    "        total_count = 0\n",
    "        property_sum = 0\n",
    "        for smiles, count in smiles_dict.items():\n",
    "            total_count += count\n",
    "            mol = Chem.MolFromSmiles(str(smiles))\n",
    "            if mol == None:\n",
    "                continue\n",
    "            else:\n",
    "                property_sum += self.calc_property(mol) * count\n",
    "        return property_sum / total_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1.\n",
      " 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n",
      " 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1.         0.         0.         0.         1.         0.\n",
      " 0.         1.         0.         1.         0.         1.\n",
      " 1.         0.         0.         0.         0.         0.86804531\n",
      " 0.52318121 0.74125186 0.         0.0301489  0.9698511  0.\n",
      " 0.         0.01550588 0.93276362 0.23637454 0.85941196 0.8788128\n",
      " 0.71412948 0.92109867 0.         0.31956348 0.05381312 0.62662339\n",
      " 0.         0.         0.06798443 0.60849347 0.48274007 0.71808093\n",
      " 0.31332415 0.6505131  0.50724298 0.38586626 0.35091049 0.\n",
      " 0.0758389  0.37553858 0.12362627 0.42499626 0.         0.\n",
      " 0.33563677 0.58154981 0.43031877 0.98231647 0.45234799 0.78455375\n",
      " 0.35690851 0.60556249]\n",
      "Number of flexible parameters: 36\n",
      "[0.86804531 0.52318121 0.74125186 0.0301489  0.9698511  0.01550588\n",
      " 0.93276362 0.23637454 0.85941196 0.8788128  0.71412948 0.92109867\n",
      " 0.31956348 0.05381312 0.62662339 0.06798443 0.60849347 0.48274007\n",
      " 0.71808093 0.31332415 0.6505131  0.50724298 0.38586626 0.35091049\n",
      " 0.0758389  0.37553858 0.12362627 0.42499626 0.33563677 0.58154981\n",
      " 0.43031877 0.98231647 0.45234799 0.78455375 0.35690851 0.60556249]\n",
      "[1.         0.         0.         0.         1.         0.\n",
      " 0.         1.         0.         1.         0.         1.\n",
      " 1.         0.         0.         0.         0.         0.85965076\n",
      " 0.13966657 0.178537   0.         0.26793775 0.37970606 0.\n",
      " 0.         0.13839166 0.58775937 0.56034311 0.00860236 0.45740702\n",
      " 0.11131542 0.62469375 0.         0.650813   0.81638465 0.90079368\n",
      " 0.         0.         0.52878815 0.89744882 0.24439425 0.42987456\n",
      " 0.82401606 0.52809851 0.01526001 0.15921581 0.49834313 0.\n",
      " 0.00125621 0.14518949 0.22135802 0.87251455 0.         0.\n",
      " 0.12249568 0.81713135 0.66355681 0.22249878 0.49620109 0.97592423\n",
      " 0.31157393 0.53505821]\n"
     ]
    }
   ],
   "source": [
    "number_flexible_parameters = len(random_weight_vector[cwg.parameters_indicator == 0.])\n",
    "print(cwg.parameters_value, cwg.parameters_indicator)\n",
    "print(random_weight_vector)\n",
    "print(\"Number of flexible parameters:\", number_flexible_parameters)\n",
    "print(random_weight_vector[cwg.parameters_indicator == 0.])\n",
    "random_weight_vector[cwg.parameters_indicator == 0.] = np.random.rand(len(random_weight_vector[cwg.parameters_indicator == 0.]))\n",
    "print(random_weight_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.        , 0.        , 0.        , 1.        ,\n",
       "       0.        , 0.        , 1.        , 0.        , 1.        ,\n",
       "       0.        , 1.        , 1.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.83672073, 0.63030105, 0.69577839,\n",
       "       0.        , 0.61742114, 0.38257886, 0.        , 0.        ,\n",
       "       0.39585807, 0.85205052, 0.21935255, 0.91777708, 0.0557885 ,\n",
       "       0.35996734, 0.64697263, 0.        , 0.58117978, 0.0204442 ,\n",
       "       0.39837602, 0.        , 0.        , 0.49230033, 0.62946299,\n",
       "       0.11659652, 0.7094888 , 0.05122692, 0.87505799, 0.8101921 ,\n",
       "       0.24838461, 0.26833378, 0.        , 0.1226163 , 0.83008269,\n",
       "       0.02601546, 0.02128556, 0.        , 0.        , 0.1884343 ,\n",
       "       0.88476531, 0.38625137, 0.83823792, 0.0487141 , 0.5298295 ,\n",
       "       0.01978145, 0.82977909, 0.56474644, 0.70324672, 0.55436731,\n",
       "       0.        , 0.03592973, 0.36495139, 0.32615915, 0.18480118,\n",
       "       0.08815855, 0.        , 0.        , 0.04631438, 0.64192163,\n",
       "       0.00295997, 0.63726499, 0.46759332, 0.7395317 , 0.41257378,\n",
       "       0.82729375, 0.35669175, 0.93050861, 0.81942357, 0.7056651 ,\n",
       "       0.33563471, 0.        , 0.41402205, 0.21361651, 0.05936374,\n",
       "       0.08273269, 0.22048125, 0.00978377, 0.        , 0.        ,\n",
       "       0.46408038, 0.57633318, 0.42467999, 0.9606278 , 0.25212139,\n",
       "       0.65418448, 0.10291175, 0.53945067, 0.36369143, 0.68475285,\n",
       "       0.21279387, 0.91566366, 0.47543614, 0.2159675 , 0.33651431,\n",
       "       0.        , 0.00882583, 0.13530532, 0.14129578, 0.42829771,\n",
       "       0.17069689, 0.10154173, 0.01403674, 0.        , 0.        ,\n",
       "       0.46638614, 0.97075288, 0.11356899, 0.79737693, 0.22112456,\n",
       "       0.71936482, 0.32968516, 0.53713137, 0.4503772 , 0.75799639,\n",
       "       0.34046663, 0.51685648, 0.26100693, 0.90538575])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cwg.apply_chemistry_constraint(random_weight_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO 11-07 11:46:10] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n",
      "[WARNING 11-07 11:46:10] ax.service.ax_client: Random seed set to 42. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n",
      "[INFO 11-07 11:46:10] ax.service.utils.instantiation: Due to non-specification, we will use the heuristic for selecting objective thresholds.\n",
      "[INFO 11-07 11:46:10] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x7', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x8', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x9', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x10', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x11', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x12', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x13', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x14', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x15', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x16', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x17', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x18', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x19', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x20', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x21', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x22', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x23', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x24', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x25', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x26', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x27', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x28', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x29', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x30', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x31', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x32', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x33', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x34', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x35', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x36', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[]).\n",
      "[INFO 11-07 11:46:10] ax.core.experiment: The is_test flag has been set to True. This flag is meant purely for development and integration testing purposes. If you are running a live experiment, please set this flag to False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda: True\n"
     ]
    }
   ],
   "source": [
    "from ax.service.ax_client import AxClient\n",
    "from ax.service.utils.instantiation import ObjectiveProperties\n",
    "from ax.modelbridge.generation_strategy import GenerationStrategy, GenerationStep\n",
    "from ax.modelbridge.registry import Models\n",
    "from ax import SearchSpace, ParameterType, RangeParameter\n",
    "from ax.core.observation import ObservationFeatures\n",
    "from ax.core.arm import Arm\n",
    "import torch\n",
    "torch.set_default_dtype(torch.float64)\n",
    "fc = FitnessCalculator(task=\"qed\")\n",
    "\n",
    "######################## Generation Strategy ###################################\n",
    "model_dict = {'MOO': Models.MOO, 'GPEI': Models.GPEI, 'SAASBO': Models.SAASBO,}\n",
    "gs = GenerationStrategy(\n",
    "    steps=[\n",
    "#         only use this when there is no initial data\n",
    "        GenerationStep(\n",
    "        model=Models.SOBOL, \n",
    "        num_trials=5,\n",
    "        max_parallelism=1,\n",
    "        model_kwargs={\"seed\": 42}, \n",
    "        ),\n",
    "        GenerationStep(\n",
    "            model=model_dict['GPEI'],\n",
    "            num_trials=-1,  # No limitation on how many trials should be produced from this step\n",
    "            max_parallelism=1,  # Parallelism limit for this step, often lower than for Sobol\n",
    "            model_kwargs = {\"torch_dtype\": torch.float64, \"torch_device\": torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"), \n",
    "                            },\n",
    "            # !!!!!!!!!!!!!!!!!! Please use the above one for real suggestion, the below is jut for test\n",
    "            # Make sure using the a number which can be divided by 16, or manually specify \"thinning\": 16 or other values\n",
    "            #model_kwargs = {\"num_samples\": 16, \"warmup_steps\": 32, \"gp_kernel\": \"matern\"},\n",
    "#             model_gen_kwargs = {\"fixed_features\": fixed_features, }\n",
    "            # \"matern\" is better for zigzag function than 'rbf'\n",
    "        ),\n",
    "    ]\n",
    ")\n",
    "ax_client = AxClient(random_seed = 42, generation_strategy = gs) # set the random seed for BO for reproducibility\n",
    "#####################################################################################\n",
    "# ax_client = AxClient()\n",
    "\n",
    "ax_client.create_experiment(\n",
    "    name=\"moo_experiment\",\n",
    "    parameters=[\n",
    "        {\n",
    "            \"name\": f\"x{i+1}\",\n",
    "            \"type\": \"range\",\n",
    "            \"bounds\": [0.0, 1.0],\n",
    "            \"value_type\": \"float\"\n",
    "        }\n",
    "        for i in range(number_flexible_parameters)\n",
    "    ],\n",
    "    objectives={\n",
    "        \"qed\": ObjectiveProperties(minimize=False,),\n",
    "        \"uniqueness\": ObjectiveProperties(minimize=False,),\n",
    "    },\n",
    "    overwrite_existing_experiment=True,\n",
    "    is_test=True,\n",
    ")\n",
    "\n",
    "def evaluate(parameters):\n",
    "    partial_inputs = np.array([parameters.get(f\"x{i+1}\") for i in range(number_flexible_parameters)])\n",
    "    inputs = random_weight_vector\n",
    "    inputs[cwg.parameters_indicator == 0.] = partial_inputs\n",
    "    inputs = cwg.apply_chemistry_constraint(inputs)\n",
    "    mg = MoleculeGenerator(num_heavy_atom, all_weight_vector=inputs)\n",
    "    smiles_dict, validity, diversity = mg.sample_molecule(num_sample)\n",
    "    # In our case, standard error is 0, since we are computing a synthetic function.\n",
    "    # Set standard error to None if the noise level is unknown.\n",
    "    return {\"qed\": (fc.calc_score(smiles_dict), None), \"uniqueness\": (diversity, None)}\n",
    "\n",
    "print(\"Using cuda:\", torch.cuda.is_available())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/beegfs/home/lungyi45/.conda/envs/qmg-n/lib/python3.12/site-packages/ax/modelbridge/cross_validation.py:463: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
      "  warn(\"Encountered exception in computing model fit quality: \" + str(e))\n",
      "[INFO 11-07 11:46:13] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.997513, 'x2': 0.104366, 'x3': 0.822979, 'x4': 0.419432, 'x5': 0.528322, 'x6': 0.056241, 'x7': 0.455292, 'x8': 0.749203, 'x9': 0.221074, 'x10': 0.617698, 'x11': 0.810095, 'x12': 0.212291, 'x13': 0.038436, 'x14': 0.881824, 'x15': 0.65063, 'x16': 0.666585, 'x17': 0.820932, 'x18': 0.596579, 'x19': 0.236308, 'x20': 0.806974, 'x21': 0.167144, 'x22': 0.767845, 'x23': 0.603483, 'x24': 0.932567, 'x25': 0.537682, 'x26': 0.398814, 'x27': 0.361339, 'x28': 0.289725, 'x29': 0.653567, 'x30': 0.234766, 'x31': 0.37597, 'x32': 0.322637, 'x33': 0.992174, 'x34': 0.348451, 'x35': 0.017679, 'x36': 0.318375} using model Sobol.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO 11-07 11:46:20] ax.service.ax_client: Completed trial 0 with data: {'qed': (0.163656, None), 'uniqueness': (0.0319, None)}.\n",
      "/beegfs/home/lungyi45/.conda/envs/qmg-n/lib/python3.12/site-packages/ax/modelbridge/cross_validation.py:463: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
      "  warn(\"Encountered exception in computing model fit quality: \" + str(e))\n",
      "[INFO 11-07 11:46:20] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.311222, 'x2': 0.605627, 'x3': 0.035712, 'x4': 0.587921, 'x5': 0.078745, 'x6': 0.899979, 'x7': 0.572131, 'x8': 0.001172, 'x9': 0.951846, 'x10': 6.9e-05, 'x11': 0.124831, 'x12': 0.652968, 'x13': 0.73864, 'x14': 0.302888, 'x15': 0.143091, 'x16': 0.368258, 'x17': 0.41325, 'x18': 0.379579, 'x19': 0.505608, 'x20': 0.182553, 'x21': 0.603845, 'x22': 0.495083, 'x23': 0.478577, 'x24': 0.255406, 'x25': 0.49407, 'x26': 0.82005, 'x27': 0.645375, 'x28': 0.612417, 'x29': 0.257426, 'x30': 0.644416, 'x31': 0.848346, 'x32': 0.746873, 'x33': 0.146935, 'x34': 0.964028, 'x35': 0.683663, 'x36': 0.930639} using model Sobol.\n",
      "[INFO 11-07 11:46:23] ax.service.ax_client: Completed trial 1 with data: {'qed': (0.348589, None), 'uniqueness': (0.006, None)}.\n",
      "/beegfs/home/lungyi45/.conda/envs/qmg-n/lib/python3.12/site-packages/ax/modelbridge/cross_validation.py:463: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
      "  warn(\"Encountered exception in computing model fit quality: \" + str(e))\n",
      "[INFO 11-07 11:46:23] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.014953, 'x2': 0.35401, 'x3': 0.554523, 'x4': 0.160723, 'x5': 0.404095, 'x6': 0.581929, 'x7': 0.042114, 'x8': 0.492718, 'x9': 0.630148, 'x10': 0.43831, 'x11': 0.350315, 'x12': 0.827958, 'x13': 0.484033, 'x14': 0.594232, 'x15': 0.449907, 'x16': 0.816458, 'x17': 0.042159, 'x18': 0.842282, 'x19': 0.881159, 'x20': 0.512417, 'x21': 0.439919, 'x22': 0.222256, 'x23': 0.87826, 'x24': 0.567615, 'x25': 0.921634, 'x26': 0.725906, 'x27': 0.17486, 'x28': 0.87341, 'x29': 0.96795, 'x30': 0.908836, 'x31': 0.202808, 'x32': 0.239301, 'x33': 0.693161, 'x34': 0.532883, 'x35': 0.758959, 'x36': 0.615213} using model Sobol.\n",
      "[INFO 11-07 11:46:26] ax.service.ax_client: Completed trial 2 with data: {'qed': (0.337475, None), 'uniqueness': (0.0195, None)}.\n",
      "/beegfs/home/lungyi45/.conda/envs/qmg-n/lib/python3.12/site-packages/ax/modelbridge/cross_validation.py:463: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
      "  warn(\"Encountered exception in computing model fit quality: \" + str(e))\n",
      "[INFO 11-07 11:46:26] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.699748, 'x2': 0.855926, 'x3': 0.337889, 'x4': 0.831959, 'x5': 0.953529, 'x6': 0.488184, 'x7': 0.922834, 'x8': 0.756436, 'x9': 0.415605, 'x10': 0.929946, 'x11': 0.53602, 'x12': 0.259847, 'x13': 0.809218, 'x14': 0.203776, 'x15': 0.941455, 'x16': 0.147951, 'x17': 0.692026, 'x18': 0.179848, 'x19': 0.36137, 'x20': 0.386777, 'x21': 0.752975, 'x22': 0.510887, 'x23': 0.000667, 'x24': 0.244412, 'x25': 0.11009, 'x26': 0.055215, 'x27': 0.833806, 'x28': 0.037048, 'x29': 0.068315, 'x30': 0.438609, 'x31': 0.542877, 'x32': 0.816064, 'x33': 0.410425, 'x34': 0.154626, 'x35': 0.413225, 'x36': 0.135769} using model Sobol.\n",
      "[INFO 11-07 11:46:32] ax.service.ax_client: Completed trial 3 with data: {'qed': (0.216229, None), 'uniqueness': (0.0204, None)}.\n",
      "/beegfs/home/lungyi45/.conda/envs/qmg-n/lib/python3.12/site-packages/ax/modelbridge/cross_validation.py:463: UserWarning: Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
      "  warn(\"Encountered exception in computing model fit quality: \" + str(e))\n",
      "[INFO 11-07 11:46:32] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.602192, 'x2': 0.433671, 'x3': 0.140529, 'x4': 0.989813, 'x5': 0.763721, 'x6': 0.148528, 'x7': 0.201065, 'x8': 0.293101, 'x9': 0.529557, 'x10': 0.141757, 'x11': 0.389903, 'x12': 0.434244, 'x13': 0.369622, 'x14': 0.415097, 'x15': 0.765747, 'x16': 0.030691, 'x17': 0.624768, 'x18': 0.026945, 'x19': 0.854632, 'x20': 0.967775, 'x21': 0.122975, 'x22': 0.913576, 'x23': 0.838211, 'x24': 0.072968, 'x25': 0.151279, 'x26': 0.593645, 'x27': 0.091831, 'x28': 0.656296, 'x29': 0.563061, 'x30': 0.076186, 'x31': 0.122911, 'x32': 0.396054, 'x33': 0.348784, 'x34': 0.688126, 'x35': 0.516998, 'x36': 0.839879} using model Sobol.\n",
      "[INFO 11-07 11:46:36] ax.service.ax_client: Completed trial 4 with data: {'qed': (0.332361, None), 'uniqueness': (0.0224, None)}.\n",
      "[INFO 11-07 11:46:38] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.203743, 'x2': 0.171656, 'x3': 0.435315, 'x4': 0.539585, 'x5': 0.693336, 'x6': 0.0, 'x7': 0.078247, 'x8': 0.4179, 'x9': 0.393865, 'x10': 0.168525, 'x11': 0.423449, 'x12': 0.619567, 'x13': 0.053684, 'x14': 0.694679, 'x15': 0.757995, 'x16': 0.494938, 'x17': 0.377311, 'x18': 0.479165, 'x19': 1.0, 'x20': 1.0, 'x21': 0.058878, 'x22': 0.710295, 'x23': 0.925138, 'x24': 0.303085, 'x25': 0.542863, 'x26': 0.687906, 'x27': 0.050338, 'x28': 0.914471, 'x29': 0.88802, 'x30': 0.272729, 'x31': 0.034096, 'x32': 0.164382, 'x33': 0.732097, 'x34': 0.597126, 'x35': 0.737156, 'x36': 0.919244} using model GPEI.\n",
      "[INFO 11-07 11:46:41] ax.service.ax_client: Completed trial 5 with data: {'qed': (0.343462, None), 'uniqueness': (0.0162, None)}.\n",
      "[INFO 11-07 11:46:43] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 1.0, 'x2': 0.555308, 'x3': 0.217797, 'x4': 1.0, 'x5': 0.931392, 'x6': 0.449181, 'x7': 0.0, 'x8': 0.231691, 'x9': 0.742345, 'x10': 0.136401, 'x11': 0.215742, 'x12': 0.743608, 'x13': 0.928966, 'x14': 0.351214, 'x15': 0.662123, 'x16': 0.0, 'x17': 0.275169, 'x18': 0.0, 'x19': 0.994076, 'x20': 1.0, 'x21': 0.258222, 'x22': 0.653878, 'x23': 1.0, 'x24': 0.232491, 'x25': 0.231362, 'x26': 0.811879, 'x27': 0.0, 'x28': 0.912465, 'x29': 0.950895, 'x30': 0.335003, 'x31': 0.0, 'x32': 0.277798, 'x33': 0.297548, 'x34': 0.759137, 'x35': 0.790442, 'x36': 0.896191} using model GPEI.\n",
      "[INFO 11-07 11:46:47] ax.service.ax_client: Completed trial 6 with data: {'qed': (0.349643, None), 'uniqueness': (0.0217, None)}.\n",
      "[INFO 11-07 11:46:50] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 1.0, 'x2': 0.522993, 'x3': 0.0, 'x4': 1.0, 'x5': 1.0, 'x6': 0.0, 'x7': 0.027996, 'x8': 0.125434, 'x9': 0.739627, 'x10': 0.014444, 'x11': 0.175213, 'x12': 0.754929, 'x13': 0.0, 'x14': 0.274926, 'x15': 0.594187, 'x16': 0.0, 'x17': 0.275058, 'x18': 0.0, 'x19': 0.970051, 'x20': 1.0, 'x21': 0.039794, 'x22': 0.803957, 'x23': 0.967646, 'x24': 0.0, 'x25': 0.0, 'x26': 0.840684, 'x27': 0.0, 'x28': 0.920575, 'x29': 0.0, 'x30': 0.057322, 'x31': 0.0, 'x32': 0.640712, 'x33': 0.0, 'x34': 0.853194, 'x35': 0.85264, 'x36': 0.970016} using model GPEI.\n",
      "[INFO 11-07 11:46:54] ax.service.ax_client: Completed trial 7 with data: {'qed': (0.35976, None), 'uniqueness': (0.0069, None)}.\n",
      "[INFO 11-07 11:46:56] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.542302, 'x2': 0.624839, 'x3': 0.0, 'x4': 0.767906, 'x5': 0.452311, 'x6': 0.363392, 'x7': 0.099667, 'x8': 0.076842, 'x9': 0.827261, 'x10': 0.014508, 'x11': 0.143634, 'x12': 0.727773, 'x13': 0.439063, 'x14': 0.187608, 'x15': 0.39182, 'x16': 0.188325, 'x17': 0.277595, 'x18': 0.234655, 'x19': 0.962754, 'x20': 0.528314, 'x21': 0.229032, 'x22': 0.665831, 'x23': 0.876768, 'x24': 0.0, 'x25': 0.201145, 'x26': 0.796047, 'x27': 0.119555, 'x28': 0.837867, 'x29': 1.0, 'x30': 0.370613, 'x31': 0.254283, 'x32': 0.996385, 'x33': 0.0, 'x34': 0.882824, 'x35': 0.808885, 'x36': 0.968044} using model GPEI.\n",
      "[INFO 11-07 11:46:59] ax.service.ax_client: Completed trial 8 with data: {'qed': (0.358481, None), 'uniqueness': (0.0149, None)}.\n",
      "[INFO 11-07 11:47:04] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.643578, 'x2': 0.446108, 'x3': 0.00523, 'x4': 1.0, 'x5': 0.9289, 'x6': 0.259279, 'x7': 0.117836, 'x8': 0.152843, 'x9': 0.631197, 'x10': 0.025065, 'x11': 0.107317, 'x12': 0.570105, 'x13': 1.0, 'x14': 0.39473, 'x15': 0.895736, 'x16': 0.0, 'x17': 0.565953, 'x18': 0.0, 'x19': 1.0, 'x20': 1.0, 'x21': 0.172603, 'x22': 0.837213, 'x23': 0.902315, 'x24': 0.0, 'x25': 0.197884, 'x26': 0.740858, 'x27': 0.054516, 'x28': 0.935453, 'x29': 0.981279, 'x30': 0.134861, 'x31': 0.04171, 'x32': 0.068287, 'x33': 0.317217, 'x34': 0.784813, 'x35': 0.33826, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:47:07] ax.service.ax_client: Completed trial 9 with data: {'qed': (0.361592, None), 'uniqueness': (0.0091, None)}.\n",
      "[INFO 11-07 11:47:14] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.813981, 'x2': 0.411317, 'x3': 0.056089, 'x4': 0.906248, 'x5': 1.0, 'x6': 0.41444, 'x7': 0.09276, 'x8': 0.079515, 'x9': 0.695989, 'x10': 0.0, 'x11': 0.036269, 'x12': 0.753081, 'x13': 0.917679, 'x14': 0.449334, 'x15': 0.734307, 'x16': 0.100455, 'x17': 0.289081, 'x18': 0.084936, 'x19': 1.0, 'x20': 1.0, 'x21': 0.246516, 'x22': 0.649251, 'x23': 0.920362, 'x24': 0.084293, 'x25': 0.352508, 'x26': 0.864777, 'x27': 0.088153, 'x28': 1.0, 'x29': 0.728941, 'x30': 0.311147, 'x31': 0.029814, 'x32': 0.0, 'x33': 0.312588, 'x34': 0.851208, 'x35': 0.957317, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:47:17] ax.service.ax_client: Completed trial 10 with data: {'qed': (0.363785, None), 'uniqueness': (0.0035, None)}.\n",
      "[INFO 11-07 11:47:31] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.311633, 'x2': 0.31502, 'x3': 0.211177, 'x4': 0.632565, 'x5': 0.671059, 'x6': 0.291365, 'x7': 0.300413, 'x8': 0.241526, 'x9': 0.732842, 'x10': 0.224796, 'x11': 0.079334, 'x12': 0.797333, 'x13': 0.541608, 'x14': 0.587489, 'x15': 0.67796, 'x16': 0.384721, 'x17': 0.435792, 'x18': 0.35451, 'x19': 1.0, 'x20': 0.810609, 'x21': 0.273595, 'x22': 0.664675, 'x23': 0.719361, 'x24': 0.32518, 'x25': 0.434717, 'x26': 0.814023, 'x27': 0.299539, 'x28': 1.0, 'x29': 0.826084, 'x30': 0.31791, 'x31': 0.332761, 'x32': 0.340568, 'x33': 0.568307, 'x34': 0.727447, 'x35': 0.0, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:47:34] ax.service.ax_client: Completed trial 11 with data: {'qed': (0.349455, None), 'uniqueness': (0.0134, None)}.\n",
      "[INFO 11-07 11:47:46] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.516511, 'x2': 0.579959, 'x3': 0.053906, 'x4': 0.99729, 'x5': 0.869625, 'x6': 0.340905, 'x7': 0.0, 'x8': 0.185697, 'x9': 0.557626, 'x10': 0.0, 'x11': 0.699928, 'x12': 0.544421, 'x13': 0.861099, 'x14': 0.26767, 'x15': 0.760865, 'x16': 0.0, 'x17': 0.290842, 'x18': 0.021606, 'x19': 1.0, 'x20': 0.976553, 'x21': 0.17511, 'x22': 0.702938, 'x23': 1.0, 'x24': 0.0, 'x25': 0.263231, 'x26': 0.745913, 'x27': 0.0, 'x28': 0.939376, 'x29': 0.899223, 'x30': 0.314585, 'x31': 0.0, 'x32': 0.323678, 'x33': 0.178931, 'x34': 0.802728, 'x35': 1.0, 'x36': 0.843462} using model GPEI.\n",
      "[INFO 11-07 11:47:51] ax.service.ax_client: Completed trial 12 with data: {'qed': (0.335565, None), 'uniqueness': (0.0303, None)}.\n",
      "[INFO 11-07 11:48:02] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.428894, 'x2': 0.367456, 'x3': 0.212701, 'x4': 0.594357, 'x5': 0.614831, 'x6': 0.277426, 'x7': 0.0, 'x8': 0.207681, 'x9': 0.678192, 'x10': 0.022849, 'x11': 0.747736, 'x12': 0.813368, 'x13': 0.46257, 'x14': 0.450011, 'x15': 0.588558, 'x16': 0.347434, 'x17': 0.357289, 'x18': 0.387608, 'x19': 0.948007, 'x20': 0.607012, 'x21': 0.229753, 'x22': 0.594016, 'x23': 0.956001, 'x24': 0.02306, 'x25': 0.48026, 'x26': 0.777888, 'x27': 0.00141, 'x28': 0.777139, 'x29': 0.706221, 'x30': 0.415898, 'x31': 0.0, 'x32': 0.005018, 'x33': 0.34397, 'x34': 0.76357, 'x35': 0.888808, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:48:06] ax.service.ax_client: Completed trial 13 with data: {'qed': (0.336775, None), 'uniqueness': (0.038, None)}.\n",
      "[INFO 11-07 11:48:24] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.093241, 'x2': 0.241927, 'x3': 0.29413, 'x4': 0.363795, 'x5': 0.418101, 'x6': 0.262237, 'x7': 0.0, 'x8': 0.099997, 'x9': 0.634251, 'x10': 0.0, 'x11': 0.770256, 'x12': 0.349683, 'x13': 0.176629, 'x14': 0.578279, 'x15': 0.472596, 'x16': 0.590357, 'x17': 0.038375, 'x18': 0.621226, 'x19': 1.0, 'x20': 0.643104, 'x21': 0.228512, 'x22': 0.533315, 'x23': 0.943593, 'x24': 0.117699, 'x25': 0.644423, 'x26': 0.982395, 'x27': 0.082627, 'x28': 1.0, 'x29': 0.58249, 'x30': 0.522587, 'x31': 0.184619, 'x32': 0.0, 'x33': 0.478957, 'x34': 0.82144, 'x35': 0.892993, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:48:27] ax.service.ax_client: Completed trial 14 with data: {'qed': (0.340012, None), 'uniqueness': (0.0167, None)}.\n",
      "[INFO 11-07 11:48:46] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.396078, 'x2': 0.41239, 'x3': 0.004484, 'x4': 0.66382, 'x5': 0.730667, 'x6': 0.167187, 'x7': 0.0, 'x8': 0.132933, 'x9': 0.711615, 'x10': 0.0, 'x11': 0.716062, 'x12': 0.938994, 'x13': 0.562581, 'x14': 0.346218, 'x15': 0.726123, 'x16': 0.250323, 'x17': 0.480354, 'x18': 0.31606, 'x19': 1.0, 'x20': 0.736857, 'x21': 0.118393, 'x22': 0.653641, 'x23': 1.0, 'x24': 0.0, 'x25': 0.438264, 'x26': 0.715893, 'x27': 0.0, 'x28': 1.0, 'x29': 0.733735, 'x30': 0.317157, 'x31': 0.0, 'x32': 0.0, 'x33': 0.167959, 'x34': 0.943692, 'x35': 1.0, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:48:50] ax.service.ax_client: Completed trial 15 with data: {'qed': (0.32826, None), 'uniqueness': (0.0243, None)}.\n",
      "[INFO 11-07 11:49:03] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.420303, 'x2': 0.520946, 'x3': 0.201747, 'x4': 0.815802, 'x5': 0.726259, 'x6': 0.374667, 'x7': 0.0, 'x8': 0.207168, 'x9': 0.546012, 'x10': 0.0, 'x11': 0.694756, 'x12': 0.861143, 'x13': 0.617577, 'x14': 0.322191, 'x15': 0.564446, 'x16': 0.163802, 'x17': 0.080304, 'x18': 0.190764, 'x19': 1.0, 'x20': 0.631823, 'x21': 0.294776, 'x22': 0.596908, 'x23': 0.987638, 'x24': 0.0, 'x25': 0.297411, 'x26': 0.92511, 'x27': 0.0, 'x28': 0.609747, 'x29': 0.941986, 'x30': 0.431131, 'x31': 0.0, 'x32': 0.230793, 'x33': 0.253999, 'x34': 0.414584, 'x35': 0.826423, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:49:08] ax.service.ax_client: Completed trial 16 with data: {'qed': (0.354648, None), 'uniqueness': (0.0271, None)}.\n",
      "[INFO 11-07 11:49:20] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.574665, 'x2': 0.513929, 'x3': 0.186216, 'x4': 0.778201, 'x5': 0.764805, 'x6': 0.439447, 'x7': 0.0, 'x8': 0.252283, 'x9': 0.665039, 'x10': 0.060523, 'x11': 0.690735, 'x12': 0.975572, 'x13': 0.91372, 'x14': 0.337266, 'x15': 0.589999, 'x16': 0.163918, 'x17': 0.0, 'x18': 0.207459, 'x19': 1.0, 'x20': 0.580029, 'x21': 0.323484, 'x22': 0.548573, 'x23': 1.0, 'x24': 0.0, 'x25': 0.368461, 'x26': 0.986825, 'x27': 0.0, 'x28': 0.797225, 'x29': 0.881386, 'x30': 0.466082, 'x31': 0.0, 'x32': 0.0, 'x33': 0.258421, 'x34': 0.452699, 'x35': 0.798269, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:49:25] ax.service.ax_client: Completed trial 17 with data: {'qed': (0.35178, None), 'uniqueness': (0.0257, None)}.\n",
      "[INFO 11-07 11:49:33] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.483045, 'x2': 0.440123, 'x3': 0.189874, 'x4': 0.792738, 'x5': 0.744796, 'x6': 0.325546, 'x7': 0.0, 'x8': 0.242622, 'x9': 0.470306, 'x10': 0.0, 'x11': 0.73193, 'x12': 0.754119, 'x13': 0.516021, 'x14': 0.409573, 'x15': 0.598586, 'x16': 0.212762, 'x17': 0.208361, 'x18': 0.215801, 'x19': 1.0, 'x20': 0.641366, 'x21': 0.291034, 'x22': 0.615669, 'x23': 0.926917, 'x24': 0.0, 'x25': 0.324442, 'x26': 0.791143, 'x27': 0.0, 'x28': 0.668502, 'x29': 0.712589, 'x30': 0.389308, 'x31': 0.0, 'x32': 0.140743, 'x33': 0.331153, 'x34': 0.792786, 'x35': 0.772951, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:49:38] ax.service.ax_client: Completed trial 18 with data: {'qed': (0.338241, None), 'uniqueness': (0.0382, None)}.\n",
      "[INFO 11-07 11:49:53] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.010879, 'x2': 0.49536, 'x3': 0.325464, 'x4': 0.586614, 'x5': 0.480053, 'x6': 0.433288, 'x7': 0.0, 'x8': 0.235293, 'x9': 0.765418, 'x10': 0.0, 'x11': 0.666908, 'x12': 0.863967, 'x13': 0.276213, 'x14': 0.32816, 'x15': 0.41838, 'x16': 0.352802, 'x17': 0.142414, 'x18': 0.408847, 'x19': 0.91373, 'x20': 0.457374, 'x21': 0.301344, 'x22': 0.429666, 'x23': 1.0, 'x24': 0.0, 'x25': 0.480983, 'x26': 0.846203, 'x27': 0.0, 'x28': 0.390733, 'x29': 0.894929, 'x30': 0.626746, 'x31': 0.0, 'x32': 0.224513, 'x33': 0.239993, 'x34': 0.825872, 'x35': 1.0, 'x36': 1.0} using model GPEI.\n",
      "[INFO 11-07 11:49:57] ax.service.ax_client: Completed trial 19 with data: {'qed': (0.351655, None), 'uniqueness': (0.013, None)}.\n"
     ]
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    parameters, trial_index = ax_client.get_next_trial()\n",
    "    # Local evaluation here can be replaced with deployment to external system.\n",
    "    ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WARNING 11-07 11:54:49] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column.\n"
     ]
    }
   ],
   "source": [
    "trial_df = ax_client.get_trials_data_frame()\n",
    "trial_df['trial_index'] = trial_df['trial_index'] - 4 \n",
    "trial_df = trial_df.iloc[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n",
      "findfont: Font family 'Arial' not found.\n"
     ]
    },
    {
     "data": {
      "image/png": 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K7zGadUk4IQR++eUXLtJNRESKJSsx7t+/Hy+++CL++OMPXdmtW7fQqVMn+Pn5oVmzZhg3bhyKiorMFigREVUvSSXJ3pRMVmLctGkT4uLidMvBAcD8+fNx9epVBAcHo1OnTti7dy+2bNlitkCJiKia2eioVFmJ8aeffkL37t11v2dmZuKrr77C2LFjcfz4cXz//fdo27YtEyMRkYJJksweoy0mxtTUVIPnH54+fRqFhYV4/vnnAQAODg4YOHAg4uPjzRMlERFVPxvtMcoalarRaPDw4UPd7ydPnoRKpULfvn11ZQ4ODsjOzq58hEREZBkqyJyuYfZIqpWsxOjn54fDhw/j7bffhp2dHXbt2oWAgACDe463b9+Gp6en2QIlIiIyJjs7G6tXr0ZERARSUlKg1WoN9v/6668mtScrMc6dOxfPPfccGjdurOsZvv322wZ1zp07h65du8ppnoiIrIBSVr6ZPn06oqKiMHHiRHh7e1f69WUlxtGjR2Pjxo0ICwsDULzyzeTJk3X7o6KikJGRgSFDhlQqOCIisiCFrHzzzTff4KuvvjJ4kEVlyF75ZtasWZg1a5bRfUFBQQZzHImISIEUsvJNnTp14OHhYbb2FH6LlIiIqoqkkr9Vp7feegvLli1DTk6OWdqT3WMsLCzEhx9+iN27d+P69evIyclBYWEhACAuLg6ffvop5s2bh9atW5slUCIiqmYK6TGuWbMG8fHx8PT0RLNmzeDg4GCw/8KFCya1JysxPnr0CIMGDcKZM2dQr149aDQag6kZvr6+2Lp1Kzw8PEoNyiEiIjKnUaNGmbU9WYnxnXfeQXR0NFavXo1FixZh5cqVeOutt3T73d3dERQUhKNHjzIxEhEplNx1T6t7rdTly5ebtT1ZifGLL75AcHAwFi9eDMD40NzmzZvz0VNEREqmkEupJWJjY3Ht2jUAQPv27dGlSxdZ7chKjHfu3MEzzzxTbh03Nzekp6fLCoqIiKyAQqZrpKSkYNy4cYiMjETt2rUBAGlpaQgODkZ4eDjq169vUnuyxg65ubkhJSWl3Drx8fEmB0NERNajZIK/nK06zZkzB5mZmbh69SpSU1ORmpqKK1euICMjA3PnzjW5PVk9xp49e+Lw4cNIS0vTZWd9iYmJ+Prrr5/YqyQiIiumkB7jkSNHcPz4cbRt21ZX1q5dO2zcuBGDBg0yuT1ZPcZFixbhjz/+QEhICKKjo3XTNHJychAREYHBgwejsLAQCxYskNM8ERFRhWm12lJTNIDih1k8vm5qRcjqMfbr1w8fffQRXn31VfTr109X7ubmBgCws7PDxx9/jICAADnNExGRVZD7CKnq7TEOGDAAr776Knbv3o2GDRsCAH7//XfMnz8fISEhJrdXqSXh+vfvj82bNyMmJgapqanQaDTo0aMH/vKXv6B9+/ZymyYiIiuglEXEP/roI/zpT39Cs2bN4OPjA6D4ll6HDh2wY8cOk9uTnRgBoG3btli/fn1lmiAiImulkHuMPj4+uHDhAo4fP47r168DKM5PoaGhstqrVGIkIqKaSyk9xpLXHDhwIAYOHFjptiqVGJOSkhAbG4u0tDQUFRUZrfPiiy9W5iWIiIhK2bBhA1566SU4OTlhw4YN5dY1dcqGJIQQpgaUm5uLGTNmIDw8vMwRP0IISJJUZsK0pIyMDLi7uyM9PR0ajcbS4RARVZo5v9dK2rr/QjA0atP7Txn5hai/42SVfsf6+vri/PnzqFu3Lnx9fcusJ0kSfv31V5PaltVjXLJkCXbu3InWrVvj+eefR+PGjWFvz6uyREQ1ihUvCZeQkGD0Z3OQlc327NmDdu3aITY2Fo6OjmYNiIiIrINSFhF/88038de//hUuLi4G5Y8ePcI//vEPLFu2zKT2ZE3wT0tLw5AhQ5gUiYhqspIeo5ytGq1cuRJZWVmlynNycrBy5UqT25PVY2zTpg2Sk5PlHEpEREqhgszpGmaPpFwlY1oe9+OPP8LDw8Pk9mQlxkWLFmHmzJm4efMmWrZsKacJIiKiSqlTp45uSknr1q0NkmNRURGysrIwc+ZMk9uVlRgbN26MwYMHo3v37pg3bx66du1a5sgj/SXjiIhIOax9HuMHH3wAIQSmTp2KlStXwt3dXbdPrVajWbNmCAwMNLldWYmxf//+kCQJQgisWLGi3JNgjdM1iIioAqx85ZtJkyYBKJ660atXL6MLicshKzEuW7bMIisbEBFRNbLi6Rr6goKCdD/n5uYiPz/fYL+pcyllJcYVK1bIOYyIiJREIYkxJycHixcvxp49e/Dw4cNS+029clnNY4eIiEg55E7VqN7EuGjRIpw4cQKbNm2Co6Mj/vnPf2LlypVo2LAhtm/fbnJ7lVqu5uLFi9i9ezeuX7+OnJwcHD9+HABw+/ZtxMTEIDQ0VNZQWSIiooo6fPgwtm/fjv79+2PKlCno27cvWrZsiaZNm2Lnzp2YMGGCSe3JToyLFy/GmjVrULLUqv49RyEExo8fjzVr1uDVV1+V+xJERGRJKlXxJue4apSamormzZsDKL6fmJqaCgDo06cPZs2aZXJ7sqLfunUr3n//fTz99NO4dOkSli5darC/WbNm6N69O/7zn//IaZ6IiKyBQla+ad68uW69VD8/P+zZswdAcU+ydu3aJrcnKzF+/PHHaNu2Lfbt24cOHTpArVaXquPn54dffvlFTvNERGQNFJIYp0yZgh9//BFA8UMuNm7cCCcnJ8yfPx+LFi0yuT1Zl1J/+uknzJgxo9wnanh6eiIlJUVO80REZA0UMip1/vz5up9DQ0Nx/fp1xMbGomXLlujUqZPJ7clKjPb29qXmiTzu7t27qFWrlpzmiYjIGijgHmNBQQGGDBmCzZs3o1WrVgCApk2bomnTprLblBV9x44dceLEiTLnhpSMUA0ICJAdGBER0ZM4ODjg0qVLZm1TVmKcOnUqfv75Z8ycORN5eXkG+zIyMjB58mQkJSVhxowZZgmSiIgsQCH3GF944QWEhYWZrT1Zl1KnTp2K48ePIywsDF988YVu1E/37t1x7do1ZGdnY/LkyRgzZozZAiUiomqmkHuMhYWF2LJli+5Kpaurq8H+tWvXmtSe7HmMu3btQnBwMD766CNcuXIFQgicP38ebdu2xdy5c/Hyyy/LbZqIiKyBQhLjlStX0LVrVwDAzz///FgopsdSqZVvZsyYgRkzZuDRo0f4448/oNFoOOCGiKimUMDgGwA4efKkWdurVGIs4ezsDGdnZ3M0RURE1kIhPcYSN2/eRHx8PPr16wdnZ2cIIWT1GLmIOBERKdrDhw8REhKC1q1bY9iwYbh37x4AYNq0aVi4cKHJ7clKjCqVCnZ2dk/cylsAgIiIrJwEmaNSqzfM+fPnw8HBAXfu3IGLi4uufOzYsThy5IjJ7cnKXP369TPaPU1PT8cvv/yC7Oxs+Pv7y1qjjoiIrIRCLqUeO3YMR48eRePGjQ3KW7Vqhdu3b5vcnqzEGBkZWea+nJwcLFmyBEeOHMG3335rcturVq3C/v37cf36dTg7O6NXr15499130aZNG12d3NxcLFy4EOHh4cjLy8PgwYPx8ccfw9PTU87bISIiIySVCpKMgTRyjqmM7Oxsg55iidTUVDg6Oprcntmjd3FxwYYNG+Du7i5r8daoqCjMnj0b586dw7fffouCggIMGjQI2dnZujrz58/H4cOHsXfvXkRFReHu3bt49tlnzfk2iIhIIQ8q7tu3r8EDiSVJglarxXvvvYfg4GCT26uym4B9+/bFjh07TD7u8evB27ZtQ4MGDRAbG4t+/fohPT0dYWFh2LVrFwYMGACg+DFYbdu2xblz59CzZ0+zxE9EZPMUcin1vffeQ0hICM6fP4/8/HwsXrwYV69eRWpqKqKjo01ur8r6u/fv30dWVlal20lPTwcAeHh4AABiY2NRUFCA0NBQXR0/Pz80adIEZ8+erfTrERGRsnTo0AE///wz+vTpg5EjRyI7OxvPPvssLl68iBYtWpjcntl7jFqtFjt37sQXX3yBp556qtJtzZs3D71790aHDh0AAElJSVCr1aUG9nh6eiIpKcloO3l5eQZrumZkZFQqLiIim6CQHuOdO3fg4+OD//u//zO6r0mTJia1JysxNm/e3Gh5YWEhUlJSUFBQAAcHB6xatUpO8zqzZ8/GlStXcPr06Uq1s2rVKqxcubJSbRAR2RyFrHzj6+uLe/fuoUGDBgblDx8+hK+vb5lPgiqLrOi1Wi2EEKU2BwcHdOjQAS+99BJiY2MRFBQkp3kAwCuvvIIvv/wSJ0+eNBiC6+Xlhfz8fKSlpRnUT05OhpeXl9G2li5divT0dN2WmJgoOy4iIpuhkKdrlLXCTVZWFpycnExuT1aP8datW3IOqxAhBObMmYMDBw4gMjISvr6+BvsDAgLg4OCAiIgIjB49GgBw48YN3LlzB4GBgUbbdHR0lDVkl4jIpln5pdQFCxb89+UkvPHGGwZTNoqKihATE4POnTub3K7VLU0ze/Zs7Nq1C4cOHYKbm5vuvqG7uzucnZ3h7u6OadOmYcGCBfDw8IBGo8GcOXMQGBjIEalEROZk5Ynx4sWLAIo7VJcvX4ZardbtU6vV8Pf3x1//+leT27W6xLhp0yYAQP/+/Q3Kt27dismTJwMA1q1bB5VKhdGjRxtM8CciIttR8lSNKVOmYMOGDXBzczNLu7ISY8n8QVNJkoSIiIhy6wghntiOk5MTNm7ciI0bN8qKg4iIKsDKB9/oL+wyadKkMuvt37/fpHYrtSScJElGE1l55UREpBBWfinV3d29StqVlRgfPXqE5557Dr/88gtef/119O3bF56enkhOTsapU6fw97//Ha1bt8aePXs46IWISKmsPDFu3bq1StqV1d9dvnw5Ll++jB9++AETJkxAkyZN4OjoiCZNmuCFF15ATEwMfvzxRyxfvtzc8RIRUXUpuZQqZ1MwWdHv2rULo0ePRq1atYzu12g0GD16NHbv3l2p4IiIyIIU8jxGc5OVGO/fv4+CgoJy65SsgkNERKQkshJjixYtsHfvXjx8+NDo/vv372PPnj1o2bJlpYIjIiILUsjKN+YmKzHOmzcPSUlJ6Nq1K9avX4/Y2FgkJiYiNjYWH3zwAQICApCSkoL58+ebO14iIqouNpoYZY1KnT59Ou7du4e33npLtyRPCSEE7OzssGLFCkydOtUsQRIRkQVIMgfSSMoefCN75Zs33ngD48ePx86dO3Hp0iWkp6fD3d0d/v7+GD9+vKxnYBERkRWx8ukaVaVSS8K1aNECy5YtM1csRERkTWw0MSq7v0tERGRmFeoxnjp1CgDQvXt3ODk56X6viH79+smLjIiILEtSybtfaAv3GPv37w9JknDt2jW0bt1a93tFmPrkZCIishIqqXiTc5yCVSgxLlu2DJIkoV69ega/ExFRDcYeY9lWrFhR7u9ERFQD2ejgG6t7UDEREVkJK38eY1VRdvRERERmJjsxHj9+HMOGDUP9+vXh4OAAOzu7Upu9PTukRESKxSXhKm7fvn0YO3YstFotmjZtCj8/PyZBIqKahoNvKu7NN9+Es7MzDh06hAEDBpg7JiIisgYlz2OUc5yCyUqMN27cwMSJE5kUiYhqMhsdfCMrMdatWxcuLi7mjoWIiKyJjU7XkJXWx4wZg+PHj6OwsNDc8RAREVmUrMT4zjvvoHbt2hg7dizu3Llj7piIiMgalAy+kbMpmKxLqR07dkRBQQHOnTuHgwcPonbt2nB3dy9VT5IkxMfHVzpIIiKyAEnmWqkKv5QqKzFqtVrY29ujSZMmujIhRKl6xsqIiEghOF2j4m7dumXmMIiIyOrY6OAbzsonIiLjbLTHqOzoiYiIzKzCPcapU6eWu1+lUsHd3R2dOnXCM888A41GU+ngiIjIgvig4vJt27atQvUkScK8efMQFhaGZ599Vm5cRERkabzHWL6TJ0+Wu1+r1eLBgwc4c+YMwsLCMH78eMTExMDf37/SQRIRkQXY6D3GCifGoKCgCtV77rnnMHXqVHTr1g1r1qzB9u3bZQdHREQWxEup5tOxY0f86U9/wqlTp6qieSIiqg6SJLPHqOzEWGX93datWyMpKamqmiciIqoSVTaPMTc3F2q1uqqaJyKiqsbBN+YVFRWFli1bVlXzRERU1Wx08I3Zo9dqtVi5ciUuXLiAUaNGmbt5IiKqLiWDb+RsCma2Cf5arRYPHz7EDz/8gPv376NVq1aYP39+pQMkIiILsdEeo9kn+KvVakyYMAFr1qyBm5ub3LiIiIgswmwT/EuWhGvTpg0cHR0rHRgREVkYB9+Ur6IT/ImIqIZQqYo3OccpGB87RUREZZDZY4SN9BiJiMjGcPANERGRHhu9x6jstE5ERGRm7DESEZFxHHxDRESkx0YvpTIxEhGRcTb62CkmRiIiMo49RiIiIj02Ol1D2dETERGZGXuMRERknNxHSNnKY6eIiMjG2OilVCZGIiIyjoNviIiI9LDHSERE9D+SJEGS0fuTc4w1UXZaJyIiMjOrS4ynTp3CiBEj0LBhQ0iShIMHDxrsF0Jg2bJl8Pb2hrOzM0JDQ/HLL79YJlgiopqs5FKqnE3BrC767Oxs+Pv7Y+PGjUb3v/fee9iwYQM2b96MmJgYuLq6YvDgwcjNza3mSImIajgbTYxWd49x6NChGDp0qNF9Qgh88MEHeP311zFy5EgAwPbt2+Hp6YmDBw9i3Lhx1RkqEVHNJsmcx8h7jNUnISEBSUlJCA0N1ZW5u7ujR48eOHv2bJnH5eXlISMjw2AjIqInsNEeo6KiT0pKAgB4enoalHt6eur2GbNq1Sq4u7vrNh8fnyqNk4ioRiiZxyhnUzBFJUa5li5divT0dN2WmJho6ZCIiMhKWd09xvJ4eXkBAJKTk+Ht7a0rT05ORufOncs8ztHREY6OjlUdHhFRzWKjz2NUVI/R19cXXl5eiIiI0JVlZGQgJiYGgYGBFoyMiKgGstFLqVbXY8zKysLNmzd1vyckJCAuLg4eHh5o0qQJ5s2bh7fffhutWrWCr68v3njjDTRs2BCjRo2yXNBERDURl4SzDufPn0dwcLDu9wULFgAAJk2ahG3btmHx4sXIzs7GSy+9hLS0NPTp0wdHjhyBk5OTpUImIqqZbPSxU5IQQlg6iOqWkZEBd3d3pKenQ6PRWDocIqJKM+f3Wklbf0QfhqaWq+nHZ2WjTu8Riv2OVXZ/l4iIyMys7lIqERFZCT6PkYiISA8H3xAREelhj5GIiEgPe4xERER6VKriTc5xCqbs6ImIiMyMPUYiIjJKkiRIMu4XyjnGmjAxEhGRcTa6iDgTIxERGcdRqURERPpkjkpV+PAVJkYiIjLORnuMyk7rREREZsYeIxERGWej8xiZGImIyDgbvZTKxEhERMZxSTgiIiI97DESERHpk/67yTlOuZTd3yUiIjIz9hiJiMg4XkolIiLSw8RIRESkzzbvMTIxEhFZgcS4Szi1eQvuxF6Eg7MzOo96GoFTJsC1Th3LBcUeIxERWcKR1WtxcOkKqOztoS0sBADEnz6Lb955H/MjDqOxf0fLBGabHUaOSiUisqRLh7/BwaUrAECXFAFACIGctHRsGPwMCnJzLRSdbWJiJCKyoGP/WA/Jzs7oPlFUhIzkFJz/Yl81R1VCqsSmXEyMREQWUlRQgJvfnYEoKiqzjsrODtePR1ZfUPpK7jHK2RSM9xiJiCxECPHkOgC05STOKiVB5uAbs0dSrdhjJCKyEHu1Go06dYBUzmOahFaL5r16VGNU+ngplYiIqlnogtkQWq3RfZJKBUdXV/R88flqjqokANu8lMrESERkQT1fHI8+L00BUHw/sYTK3g52agfMPLATzhqNpcKzSbzHSERkQZIkYcLmD9Bh2CBEfvQpEi/+CAcnJ3QZ/ScEz3kZDVq2sGR0sMWJjEyMREQWJkkSOo8cjs4jh1s6FENc+YaIiEgPEyMREZE+Xkql8ghRvAE1YtQVEdGTSJIEScZ3nZxjrAkT45MIARTmA4V5honR3hGwVzNBEhHVMEyM5RECyH8EFBWULi/IBbRFgNqZyZGIaibeY6RStIWlk6K+ogJA6wDYOVRfTERE1Yb3GOlxBfkVq8PESEQ1ktzxFEyMNZeowMK9FalDRKREvJRKpUkoXtv+SXWIiGoi27yUyrVSy2NfgUuk9uqqj4OIiKoNe4zlsVf/9z5jWb1GqWLJk4hIiWz0Uip7jOWRVICTq/F/ZEn67z6eQiKqoWzzcYzsMT6Ryg5wcgOKCounbwCAyh6ws1f8X0VEROWzzXuMTIwVIZVcMuVlUyKyIbyUSkREROwxEhGRcTbaY2RiJCKiMvAeIxER0f9IkNljNHsk1Yr3GImIyLiSS6lyNhOcOnUKI0aMQMOGDSFJEg4ePFg176eCmBiJiKgM1TORMTs7G/7+/ti4caPZIq8MXkolIiKLGjp0KIYOHWrpMHRsMjEKUbzEW0ZGhoUjISIyj5Lvs5LvN7O0mZUl6x5jRlaWQUwlHB0d4ejoaJbYqpJNJsbMzEwAgI+Pj4UjISIyr8zMTLi7u1eqDbVaDS8vL/i0bi+7jVq1apX6jl2+fDlWrFhRqdiqg00mxoYNGyIxMRFubm6QTPhrKCMjAz4+PkhMTIRGo6nCCJWF56VsPDfG8byUTe65EUIgMzMTDRs2rHQMTk5OSEhIQH5+BR7WXk48j3+/KqG3CNhoYlSpVGjcuLHs4zUaDf9jNoLnpWw8N8bxvJRNzrmpbE9Rn5OTE5ycnMzWnpJwVCoREZEem+wxEhGR9cjKysLNmzd1vyckJCAuLg4eHh5o0qRJtcfDxGgCR0dHLF++XDHXyasLz0vZeG6M43kpmy2em/PnzyM4OFj3+4IFCwAAkyZNwrZt26o9HkmYc2wvERGRwvEeIxERkR4mRiIiIj1MjERERHqYGImIiPQwMeqR8+iTyMhIdO3aFY6OjmjZsqVFRlBVNVPPS2RkJCRJKrUlJSVVT8DVZNWqVejWrRvc3NzQoEEDjBo1Cjdu3HjicXv37oWfnx+cnJzQsWNHfP3119UQbfWRc162bdtW6vNSEyeXb9q0CZ06ddJN3g8MDMQ333xT7jE1/fNijZgY9Zj66JOEhAQMHz4cwcHBiIuLw7x58zB9+nQcPXq0iiOtXnIfCXPjxg3cu3dPtzVo0KCKIrSMqKgozJ49G+fOncO3336LgoICDBo0CNnZ2WUec+bMGTz//POYNm0aLl68iFGjRmHUqFG4cuVKNUZeteScF6B4pRf9z8vt27erKeLq07hxY6xevRqxsbE4f/48BgwYgJEjR+Lq1atG69vC58UqCTIKgDhw4EC5dRYvXizat29vUDZ27FgxePDgKozMsipyXk6ePCkAiD/++KNaYrIWKSkpAoCIiooqs86f//xnMXz4cIOyHj16iJdffrmqw7OYipyXrVu3Cnd39+oLyorUqVNH/POf/zS6zxY/L9aAPcZKOHv2LEJDQw3KBg8ejLNnz1ooIuvSuXNneHt7Y+DAgYiOjrZ0OFUuPT0dAODh4VFmHVv8zFTkvADFq580bdoUPj4+5faiaoqioiKEh4cjOzsbgYGBRuvY4ufFGjAxVkJSUhI8PT0Nyjw9PZGRkYFHjx5ZKCrL8/b2xubNm7Fv3z7s27cPPj4+6N+/Py5cuGDp0KqMVqvFvHnz0Lt3b3To0KHMemV9Zmra/dcSFT0vbdq0wZYtW3Do0CHs2LEDWq0WvXr1wm+//VaN0VaPy5cvo1atWnB0dMTMmTNx4MABtGvXzmhdW/u8WAsuCUdm16ZNG7Rp00b3e69evRAfH49169bhX//6lwUjqzqzZ8/GlStXcPr0aUuHYlUqel4CAwMNek29evVC27Zt8cknn+Ctt96q6jCrVZs2bRAXF4f09HT8+9//xqRJkxAVFVVmcqTqxx5jJXh5eSE5OdmgLDk5GRqNBs7OzhaKyjp1797dYJHgmuSVV17Bl19+iZMnTz7xcWZlfWa8vLyqMkSLMOW8PM7BwQFdunSpkZ8ZtVqNli1bIiAgAKtWrYK/vz/Wr19vtK4tfV6sCRNjJQQGBiIiIsKg7Ntvvy3zfoEti4uLg7e3t6XDMCshBF555RUcOHAAJ06cgK+v7xOPsYXPjJzz8riioiJcvny5xn1mjNFqtcjLyzO6zxY+L1bJ0qN/rElmZqa4ePGiuHjxogAg1q5dKy5evChu374thBBiyZIlYuLEibr6v/76q3BxcRGLFi0S165dExs3bhR2dnbiyJEjlnoLVcLU87Ju3Tpx8OBB8csvv4jLly+LV199VahUKnH8+HFLvYUqMWvWLOHu7i4iIyPFvXv3dFtOTo6uzsSJE8WSJUt0v0dHRwt7e3vx/vvvi2vXronly5cLBwcHcfnyZUu8hSoh57ysXLlSHD16VMTHx4vY2Fgxbtw44eTkJK5evWqJt1BllixZIqKiokRCQoK4dOmSWLJkiZAkSRw7dkwIYZufF2vExKinZJrB49ukSZOEEEJMmjRJBAUFlTqmc+fOQq1Wi+bNm4utW7dWe9xVzdTz8u6774oWLVoIJycn4eHhIfr37y9OnDhhmeCrkLFzAsDgMxAUFKQ7TyX27NkjWrduLdRqtWjfvr346quvqjfwKibnvMybN080adJEqNVq4enpKYYNGyYuXLhQ/cFXsalTp4qmTZsKtVot6tevL0JCQnRJUQjb/LxYIz52ioiISA/vMRIREelhYiQiItLDxEhERKSHiZGIiEgPEyMREZEeJkYiIiI9TIxERER6mBiJSPGEEAgICMCgQYMqfMzrr78ONze3UmuREjEx1hBTp06FJEmoW7dumesukmmSkpLw2muvoVOnTnBzc4OLiwvatGmDOXPmICEhwegx27ZtgyRJ5W6TJ0/W1b9161ap/S4uLmjYsCFCQkKwbNkyxMfHV9M7Vq7t27fjwoULePPNNyt8zMKFC6FSqbB8+fIqjIyUiCvf1ACZmZnw9vZGTk4OhBAIDw/H2LFjLR2Won399dcYN24cMjMz0bNnTwQGBsLe3h5xcXGIiIiAg4MDtmzZgvHjxxsct23bNkyZMgUhISHo06eP0bY7d+6MUaNGAShOjL6+vmjRogVeeOEFAEBeXh5SUlLw/fff48qVK7Czs8PixYvx97//HZIkVen7ViKtVosWLVrAx8cHp06dMunYhQsXYv369YiPj0fTpk2rKEJSHEuuR0fm8dlnnwkAYsGCBUKlUomBAwdaOiRFO3/+vHB0dBTOzs7i4MGDpfafOXNG1KtXT6hUqlJrwG7dulUAEKtWrarQayUkJAgAYvDgwUb3f/fdd6JZs2YCgHj99ddNfzM24MsvvxQAxGeffWbysRcuXOC5pVKYGGuAnj17Cnt7e5GUlCRCQkKESqUSt27d0u3Pzs4WtWrVEs2bNy+zjY4dOwonJyeRnp6uK9NqtSIsLEz06tVLuLm5CWdnZxEQECDCwsJKHb98+XIBQJw8eVJs3bpVdOnSRTg7O+sWF09LSxOrV68W/fr1E97e3sLBwUF4e3uLiRMnips3bxqN6f79+2LGjBmifv36wtnZWTz11FNi//79uuRjbMH2H3/8UYwdO1Z4eXkJBwcH0aRJE/HKK6+IBw8eVPBsCtG7d+8nftEeO3ZMABBt27YVWq1WV27uxCiEENevXxeOjo5CrVaLO3fuVKjdEydOiCFDhghvb2+hVqtFgwYNRJ8+fcQnn3xSqm58fLyYMWOGaNasmW5x66CgIKPnd8uWLaJ79+7C1dVVuLq6iu7duxutV7Lw/PLly0V0dLQYOHCgcHd3F/p/i5vy+SrP6NGjhSRJIjU11aTjSrRs2VI0btxY1rFUMzExKtzVq1cFADFs2DAhhBCff/657gtJ36RJkwQAER0dXaqNuLg4AUCMHTtWV6bVasXzzz8vAIhWrVqJl19+WcyZM0f4+fkJAGLhwoUGbZQkxmHDhglnZ2cxbtw48dprr4m//e1vQgghzp49K9RqtRg8eLD4y1/+IhYtWiRGjBgh7OzshIeHh0EiF6L4UVft2rUTAESvXr3EkiVLxAsvvCDUarUYMWKE0cR46NAhXU9v3LhxYtGiRWL48OG691CRL86ff/5ZABCNGjUShYWF5dbt0aOHACBOnTqlK6uKxChE8eOIAIgNGzY8sc0vv/xSSJIk6tSpIyZPniyWLl0qpk+fLrp16yb69OljUPe7774TGo1GSJIkhgwZIpYsWSJefvll0b17d9G5c2eDunPmzNGdm7lz54q5c+eKRo0aCQBi7ty5BnVLEuPAgQOFg4ODGDRokFi0aJHuM2bq56ssWq1WeHh4CD8/vwrVN6bk3N64cUN2G1SzMDEq3IIFCwQAsXv3biFEcUJxdXUVTZo0EUVFRbp6x48fFwDErFmzSrWxcOFCAUB8+eWXurJPP/1UABBTpkwR+fn5uvK8vDxdYjp//ryuvCQxurq6ikuXLpV6jbS0NPHw4cNS5SdOnBAqlUpMnz7doPz1118XAMRLL71kUF7yPh5PjA8ePBAajUY0atSoVJLdvXu3ACBeeeWVUq//uG3btgkAYsKECU+s+7e//a1UEixJjCEhIWL58uVGt2vXrunqVzQxhoWFCQAGz70sy7PPPisAiLi4uFL79HvOubm5olGjRkKlUolvvvmmVN3ExETdz1FRUboeclpamq48NTVVtG7dutQfCPqPKtuyZUuptk39fJWl5A/Divx7lWX9+vVlxkm2iYlRwfLz80X9+vWFRqMRjx490pW/8MILAoA4evSorqyoqEg0atRI1K1b1+CLqKioSHh7e4v69euLgoICXXmnTp2Eq6urwcNlS1y6dKnUX/UliXH+/Pkmv4+OHTuKZs2aGZSVXNZLSkoqVX/QoEGlEuPatWsFALF9+3ajr9G1a1dRr169J8ayevVqAcDgYbFl2bRpU6mEW5IYy9sOHDigq1/RxPjNN98IAGLo0KFPjKskMT6pB/TFF18IAOLFF198YptTp04VAMQXX3xRat/OnTsFADF16lRdWUli7Nq1q9H2TP18leXo0aO6++tyhYeHCwDizTfflN0G1Sz2pg3VIWty6NAh3L9/H9OmTYOTk5Ou/MUXX8SOHTsQFhamm9elUqkwYcIEvPfee/j6668xcuRIAEBERATu3buHOXPmwN6++OOQk5ODy5cvo2HDhnj33XdLvW5BQQEA4Pr166X2de/evcx4IyMj8cEHHyAmJgYPHjxAYWGhbp9ardb9nJGRgVu3bqFdu3bw9PQs1U7v3r1x7Ngxg7Jz584BAGJiYoxOb8jNzcWDBw/w4MED1KtXr8wY5dBqtaXKVq1ahSVLlpj1dSpq3Lhx2L9/P3r27Inx48cjJCQEffv2LfW+v//+ewCo0Ny/ixcvAgD69+9fal9wcDAAIC4urtS+bt26lSqrzOfrcQ8fPgQA1K5du9S+yMhIREZGGpTpjwgu4eHhAQB48ODBE1+PbAMTo4KFhYUBKE6E+kJCQtCoUSMcOnQIqampuv/wJ06ciPfeew87duzQJcZ//etfun0l/vjjDwgh8Pvvv2PlypVlvn52dnapMmOJDAD27t2LsWPHolatWhg8eDCaNWsGFxcXSJKEbdu24fbt27q6GRkZAIAGDRoYbcvYa6SmpgIANm7cWGa8JTGXlxi9vLwAAImJieW2o1+nUaNGT6xbWXfv3gUA1K9f/4l1n3vuORw8eBBr167F5s2bsXHjRkiShODgYKxZswadO3cGAKSnpwOoWPwZGRlQqVRGX9/T0xOSJOn+3R7f97jKfL4e5+zsDKD4D5/HRUZGlmp/0qRJpRLjo0ePAAAuLi5PfD2yDUyMCpWYmKjrNQUFBZVZb8eOHZg7dy4AoEOHDujcuTO+/PJLpKenw8HBAQcOHECbNm0M/rLXaDQAgICAAJw/f96kuMqaZ7dixQo4OTkhNjYWrVq1MtgXHh5u8HvJ66ekpBhty9hKJSXHXL58GR06dDApZn29evUCUPylWlRUBDs7uzLrRkREAADatWsn+/UqqqTnY6wHZszIkSMxcuRIZGZmIjo6Gvv370dYWBiGDBmC69evo3bt2rpe1u+///7E9jQaDbRaLe7fv1/qD5aUlBQIIXT/BvqMfR4q8/l6XEmiLvnDSN+KFSuwYsWKJ7ZRcmxF/ugg28CVbxRq27Zt0Gq16NOnD6ZNm1ZqmzRpEoD/9SpLTJw4Ebm5ufj3v/+NAwcOICsrSzexvISbmxvatm2La9euIS0tzSzxxsfHo23btqWS4r179/Drr78alGk0GjRr1gw3b940mhzPnDlTqqxHjx4AgLNnz1YqzlatWqFPnz74/fff8fnnn5dZLyIiAjExMahXrx6GDRtWqdd8kp9//hl79uyBo6MjnnnmGZOOdXNzw5AhQ/Dpp59i8uTJSE5ORkxMDID/XfZ+/LK0MV26dAGAUpcm9ctKeqIViclcn6/27dtDpVLhxo0bstsoObZjx46VioVqEAvf4yQZtFqt8PX1FZIkifj4+DLrBQYGCgDihx9+0JXdu3dP2NnZiaCgIDF48GAhSZJISEgodWzJwJIxY8aIrKysUvt//fVXg+P05zEa07p1a6HRaAwG0zx69EiMHDlSNyhF3//93/8JAGLmzJkG5fqjHfUH36SkpAg3NzdRv359ceXKlVKvn52dLc6ePWs0tsfFxsYKR0dH4eLiIg4fPlxqf0xMjGjQoIEAINauXWuwz9zTNU6fPq2b4P/4FJyyREVFGZ1q8vTTTwsAIjIyUghRPCq1cePGQqVSiSNHjpSq/9tvvxm0CUC0a9fOYK5rWlqabopFVFSUrlx/HqMxpn6+ytO5c2fh5uZmMArbFMHBwcLe3t5oHGSbeClVgU6cOIGEhAQEBQWhefPmZdabMmUKzp49i7CwMDz11FMAiu+hhYaG4tixY1CpVOjTpw+aNWtW6tiXX34Z586dw+eff47o6GiEhoaiYcOGSE5OxvXr1xETE4Ndu3YZPdaYOXPmYM6cOejSpQvGjBmDwsJCfPvttxBCwN/fHz/++KNB/ddeew379u3D5s2bceXKFfTt2xe//fYb9uzZgxEjRuDw4cNQqf53waN+/frYvXs3nnvuOfj7+2PIkCHw8/NDXl4ebt26haioKPTq1QtHjhx5Yqxdu3bFvn37MG7cOIwYMQKBgYEGS8IdP34cWq0WU6ZMwfz58422cfz4caP3vYDif4OZM2calN28eVN32S8/P1+3JNzly5dhZ2eH119/vcJres6dOxd3797V/dtKkoTTp0/j+++/R8+ePXVL1Tk6OmLPnj0YMmQIhg4diiFDhsDf3x8ZGRmIi4tDTk6ObtBNv379MGfOHHz44Yfo0KEDRo8eDSEE9u3bh99++w1z585Fv379KhQfYN7P1zPPPIPly5fj3LlzukvhFZWVlYVz585h4MCBcHV1NelYqsEsnZnJdCUTo42tOKIvPT1dODs7C3d3d4Nh8Tt27ND1uoythKLviy++EKGhoaJOnTrCwcFBNGrUSPTv31+sWbNG3L9/X1fvST1GrVYrNm/eLNq3by+cnJyEl5eXmDZtmkhJSRFBQUGleoxCFPcCp02bJurVqyecnJxEQECA2L9/v3j//fdLTXsocf36dTFt2jTRtGlToVarRZ06dUTHjh3F3Llzxffff1/ue33c3bt3xaJFi0T79u2Fq6ur7py5urqK8PBwo8dUZLqGv7+/rn5Jj1F/c3Z2Ft7e3iI4OFi88cYbZa4MVJbw8HDx5z//WbRo0UK4uLgId3d34e/vL959912RmZlZqv7NmzfFtGnTROPGjYWDg4No0KCB6N+/v9GpL1u2bBHdunUTLi4uwsXFRXTr1s3o/L8n9RhLVPTzVZ7ff/9d2NvbG52j+yQl81aNLf1HtouLiJPivPDCC9i5cyd++ukntG3bttpeV6vV4plnnsF//vMffPjhh3jllVeq7bWpfBMnTsRXX32F27dvw83NrcLH9e3bF8nJybh27Vq5A63ItnDwDVmte/fulSqLiopCeHg42rRpU61JESieC7pz5074+/tj7ty5uqkuZHlvv/02Hj16hA8//LDCx0REROD06dN49913mRTJAO8xktUaNmwYnJ2d0blzZ7i6uuKnn37CkSNHYGdnZ9IXoDnVqlULhw8fRlhYGO7cuYOsrCzUqlXLIrHQ/zRt2hSff/65SQ8dTk9Px/vvv2/ySF+q+XgplazWBx98gJ07dyI+Ph6ZmZmoXbs2evfujaVLl+qmZxARmRsTIxERkR7eYyQiItLDxEhERKSHiZGIiEgPEyMREZEeJkYiIiI9TIxERER6mBiJiIj0MDESERHpYWIkIiLS8/90eijvEZ9p1gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define figure size\n",
    "plt.figure(figsize=(5, 4), dpi=100)\n",
    "\n",
    "# Scatter plot with color based on 'trial_index' column\n",
    "scatter = plt.scatter(trial_df['qed'], trial_df['uniqueness'], c=trial_df['trial_index'], cmap='Reds')\n",
    "\n",
    "# Adding colorbar with integer ticks\n",
    "z_min, z_max = int(trial_df['trial_index'].min()), int(trial_df['trial_index'].max())\n",
    "colorbar = plt.colorbar(scatter, ticks=np.arange(z_min, z_max + 1, 5))\n",
    "colorbar.set_label('Iteration', fontsize=14, weight='semibold')\n",
    "\n",
    "# Remove top and right spines for a cleaner look\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# Set labels with appropriate font sizes\n",
    "plt.xlabel('Average QED score (-)', fontsize=14, weight='semibold')\n",
    "plt.ylabel('Uniqueness (-)', fontsize=14, weight='semibold')\n",
    "\n",
    "# Adjust tick parameters\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Tight layout for optimal spacing\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Sample points\n",
    "x = np.linspace(0, 10, 100)\n",
    "y_true = np.sin(x / 2) * 2  # True function (unknown in BO)\n",
    "\n",
    "# Simulated observations\n",
    "x_obs = np.array([1, 3, 6, 8])\n",
    "y_obs = np.sin(x_obs / 2) * 2 + np.random.normal(0, 0.1, size=x_obs.shape)\n",
    "\n",
    "# Gaussian Process posterior mean and confidence interval (simulated)\n",
    "y_pred = np.sin(x / 2) * 2\n",
    "y_std = np.exp(-0.1 * (x - 5)**2)\n",
    "\n",
    "# Acquisition function (simulated)\n",
    "acq_func = norm.pdf(x, loc=7, scale=1.5)\n",
    "\n",
    "# Plot settings for a Nature-style figure\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10, 5))\n",
    "t_vals = [3, 4]\n",
    "\n",
    "for i, t in enumerate(t_vals):\n",
    "    ax_top = axes[0, i]\n",
    "    ax_bottom = axes[1, i]\n",
    "\n",
    "    # Posterior mean and confidence interval\n",
    "    ax_top.plot(x, y_pred, color='blue', linewidth=2)\n",
    "    ax_top.fill_between(x, y_pred - y_std, y_pred + y_std, color='blue', alpha=0.2)\n",
    "    ax_top.scatter(x_obs[:t], y_obs[:t], color='white', edgecolor='black', zorder=5, s=50, linewidth=1)\n",
    "    ax_top.set_title(f'$t={t}$', fontsize=12)\n",
    "    ax_top.set_ylabel(\"Posterior\")\n",
    "    \n",
    "    # Remove ticks and spines\n",
    "    ax_top.set_yticks([])\n",
    "    ax_top.set_xticks([])\n",
    "    ax_top.spines['top'].set_visible(False)\n",
    "    ax_top.spines['right'].set_visible(False)\n",
    "    # ax_top.spines['left'].set_visible(False)\n",
    "    # ax_top.spines['bottom'].set_visible(False)\n",
    "\n",
    "    # New observation annotation for t=4\n",
    "    if t == 4:\n",
    "        ax_top.scatter([7], [np.sin(7 / 2) * 2], color='red', zorder=6, s=50)\n",
    "        ax_top.text(7, np.sin(7 / 2) * 2 + 0.2, \"New observation\", ha='center', fontsize=10)\n",
    "\n",
    "    # Acquisition function plot\n",
    "    ax_bottom.plot(x, acq_func, color='green', linewidth=2)\n",
    "    ax_bottom.fill_between(x, 0, acq_func, color='green', alpha=0.2)\n",
    "    ax_bottom.axvline(7, color='red', linestyle='--', linewidth=1)\n",
    "    ax_bottom.text(7, np.max(acq_func), \"Next point\", color='red', ha='center', va='bottom', fontsize=10)\n",
    "    ax_bottom.set_ylabel(\"Acquisition function\")\n",
    "    \n",
    "    # Remove ticks and spines\n",
    "    ax_bottom.set_yticks([])\n",
    "    ax_bottom.set_xticks([])\n",
    "    ax_bottom.spines['top'].set_visible(False)\n",
    "    ax_bottom.spines['right'].set_visible(False)\n",
    "    # ax_bottom.spines['left'].set_visible(False)\n",
    "    # ax_bottom.spines['bottom'].set_visible(False)\n",
    "\n",
    "# General adjustments\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.1)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Sample points\n",
    "x = np.linspace(0, 10, 100)\n",
    "y_true = np.sin(x / 1) * 2  # True function (unknown in BO)\n",
    "\n",
    "# Simulated observations\n",
    "x_obs = np.array([2.4, 3., 6, 8, 1, 10, 7])\n",
    "y_obs = np.sin(x_obs / 1) * 2 + np.random.normal(0, 0.01, size=x_obs.shape)\n",
    "\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, WhiteKernel\n",
    "\n",
    "# Plot settings for a Nature-style figure\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10, 5))\n",
    "t_vals = [6, 7]\n",
    "\n",
    "for i, t in enumerate(t_vals):\n",
    "    ax_top = axes[0, i]\n",
    "    ax_bottom = axes[1, i]\n",
    "\n",
    "    # Use observations up to time t\n",
    "    x_obs_t = x_obs[:t]\n",
    "    y_obs_t = y_obs[:t]\n",
    "\n",
    "    # Reshape data for sklearn\n",
    "    X_obs_t = x_obs_t.reshape(-1, 1)\n",
    "    X = x.reshape(-1, 1)\n",
    "\n",
    "    # Gaussian Process posterior mean and confidence interval\n",
    "    kernel = RBF(length_scale=1.0) + WhiteKernel(noise_level=0.01)\n",
    "    gp = GaussianProcessRegressor(kernel=kernel)\n",
    "    gp.fit(X_obs_t, y_obs_t)\n",
    "    y_pred, y_std = gp.predict(X, return_std=True)\n",
    "\n",
    "    # Acquisition function (Expected Improvement)\n",
    "    y_max = np.max(y_obs_t)\n",
    "    xi = 0.01  # Exploration-exploitation trade-off parameter\n",
    "    mu = y_pred\n",
    "    sigma = y_std\n",
    "    with np.errstate(divide='warn'):\n",
    "        imp = mu - y_max - xi\n",
    "        Z = imp / sigma\n",
    "        ei = imp * norm.cdf(Z) + sigma * norm.pdf(Z)\n",
    "        ei[sigma == 0.0] = 0.0\n",
    "    acq_func = ei\n",
    "\n",
    "    # Posterior mean and confidence interval plot\n",
    "    ax_top.plot(x, y_pred, color='blue', linewidth=2)\n",
    "    ax_top.fill_between(x, y_pred - y_std, y_pred + y_std, color='blue', alpha=0.2)\n",
    "    ax_top.scatter(x_obs_t, y_obs_t, color='white', edgecolor='black', zorder=5, s=50, linewidth=1)\n",
    "    # ax_top.set_title(f'$t={t}$', fontsize=12)\n",
    "    ax_top.set_ylabel(\"Posterior\")\n",
    "\n",
    "    # Remove ticks and spines\n",
    "    ax_top.set_yticks([])\n",
    "    ax_top.set_xticks([])\n",
    "    ax_top.spines['top'].set_visible(False)\n",
    "    ax_top.spines['right'].set_visible(False)\n",
    "\n",
    "    # New observation annotation for t=4\n",
    "    if t == 4:\n",
    "        x_new = x_obs[t - 1]  # The new observation at time t=4\n",
    "        y_new = y_obs[t - 1]\n",
    "        ax_top.scatter([x_new], [y_new], color='red', zorder=6, s=50)\n",
    "        ax_top.text(x_new, y_new + 0.2, \"New observation\", ha='center', fontsize=10)\n",
    "\n",
    "    # Acquisition function plot\n",
    "    x_next = X[np.argmax(acq_func)]\n",
    "    ax_bottom.plot(x, acq_func, color='green', linewidth=2)\n",
    "    ax_bottom.fill_between(x, 0, acq_func, color='green', alpha=0.2)\n",
    "    ax_bottom.axvline(x_next, color='red', linestyle='--', linewidth=1)\n",
    "    ax_bottom.text(x_next, np.max(acq_func), \"Next point\", color='red', ha='center', va='bottom', fontsize=10)\n",
    "    ax_bottom.set_ylabel(\"Acquisition function\")\n",
    "\n",
    "    # Remove ticks and spines\n",
    "    ax_bottom.set_yticks([])\n",
    "    ax_bottom.set_xticks([])\n",
    "    ax_bottom.spines['top'].set_visible(False)\n",
    "    ax_bottom.spines['right'].set_visible(False)\n",
    "\n",
    "# General adjustments\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.1, hspace=0.1)\n",
    "plt.savefig(\"bo.svg\", format=\"svg\")\n",
    "plt.show()\n"
   ]
  }
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